Prediction of local scour depth downstream of sluice gates using data-driven models

doi:10.1080/09715010.2017.1286614

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Prediction of local scour depth downstream of sluice gates using data-driven models

Journal Article•DOI•

Prediction of local scour depth downstream of sluice gates using data-driven models

Mohammad Najafzadeh¹, Ali Tafarojnoruz², Siow-Yong Lim³•Institutions (3)

Graduate University of Advanced Technology¹, University of Calabria², Nanyang Technological University³

13 Feb 2017-ISH Journal of Hydraulic Engineering (Taylor & Francis)-Vol. 23, Iss: 2, pp 195-202

TL;DR: In this paper, the effects of influencing parameters on scouring process in order to derive an accurate predictive equation of local scour depth were investigated. But the results were limited to local scouring depth.

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Abstract: Numerous investigations have already been conducted to characterize the effects of influencing parameters on scouring process in order to derive an accurate predictive equation of local scour depth...

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Journal Article•DOI•

Cours d'economie politique

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Jean Marchal

01 Apr 1950-Population

601 citations

Journal Article•DOI•

River water quality index prediction and uncertainty analysis: A comparative study of machine learning models

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Seyed Babak Haji Seyed Asadollah¹, Ahmad Sharafati¹, Ahmad Sharafati², Davide Motta³, Zaher Mundher Yaseen⁴ - Show less +1 more•Institutions (4)

Islamic Azad University¹, Duy Tan University², Northumbria University³, Ton Duc Thang University⁴

01 Feb 2021-Journal of environmental chemical engineering

TL;DR: In this article, a new ensemble machine learning model called Extra Tree Regression (ETR) was introduced for predicting monthly WQI values at the Lam Tsuen River in Hong Kong.

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Abstract: The Water Quality Index (WQI) is the most common indicator to characterize surface water quality. This study introduces a new ensemble machine learning model called Extra Tree Regression (ETR) for predicting monthly WQI values at the Lam Tsuen River in Hong Kong. The ETR model performance is compared with that of the classic standalone models, Support Vector Regression (SVR) and Decision Tree Regression (DTR). The monthly input water quality data including Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Electrical Conductivity (EC), Nitrate-Nitrogen ( NO 3 -N), Nitrite-Nitrogen ( NO 2 -N), Phosphate ( P O 4 3 - ), potential for Hydrogen (pH), Temperature (T) and Turbidity (TUR) are used for building the prediction models. Various input data combinations are investigated and assessed in terms of prediction performance, using numerical indices and graphical comparisons. The analysis shows that the ETR model generally produces more accurate WQI predictions for both training and testing phases. Although including all the ten input variables achieves the highest prediction performance ( R 2 t e s t = 0.98 , R M S E t e s t = 2.99 ), a combination of input parameters including only BOD, Turbidity and Phosphate concentration provides the second highest prediction accuracy ( R 2 t e s t = 0.97 , R M S E t e s t = 3.74 ). The uncertainty analysis relative to model structure and input parameters highlights a higher sensitivity of the prediction results to the former. In general, the ETR model represents an improvement on previous approaches for WQI prediction, in terms of prediction performance and reduction in the number of input parameters.

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127 citations

Journal Article•DOI•

A new development of ANFIS–GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets

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Hooman Harandizadeh¹, Danial Jahed Armaghani², Mahdy Khari³•Institutions (3)

Shahid Bahonar University of Kerman¹, Duy Tan University², Islamic Azad University³

01 Jan 2021-Engineering With Computers

TL;DR: Modelling results indicated that improved ANFIS–GMDH model achieved relatively higher performance compared to ANN and FPNN–G MDH models in terms of accuracy and reliability level based on standard statistical performance indices.

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Abstract: Prediction of ultimate pile bearing capacity with the aid of field experimental results through artificial intelligence (AI) techniques is one of the most significant and complicated problem in pile analysis and design. The aim of this research is to develop a new AI predictive models for predicting pile bearing capacity. The first predictive model was developed based on the combination of adaptive neuro-fuzzy inference system (ANFIS) and group method of data handling (GMDH) structure optimized by particle swarm optimization (PSO) algorithm called as ANFIS–GMDH–PSO model; the second model introduced as fuzzy polynomial neural network type group method of data handling (FPNN–GMDH) model. A database consists of different piles property and soil characteristics, collected from literature including CPT and pile loading test results which applied for training and testing process of developed models. Also a common artificial neural network (ANN) model was applied as a reference model for comparing and verifying among hybrid developed models for prediction. The modelling results indicated that improved ANFIS–GMDH model achieved relatively higher performance compared to ANN and FPNN–GMDH models in terms of accuracy and reliability level based on standard statistical performance indices such as coefficient of correlation (R), mean square error, root mean square error and error standard deviation values.

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77 citations

Journal Article•DOI•

Prediction of riprap stone size under overtopping flow using data-driven models

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Mohammad Najafzadeh¹, Mohammad Rezaie-Balf¹, Ali Tafarojnoruz²•Institutions (2)

Graduate University of Advanced Technology¹, University of Calabria²

21 Feb 2018-International Journal of River Basin Management

TL;DR: A review of the literature on rock riprap is presented in this article, which is commonly used to protect levees, embankment dam, steep channels, and other structures being vulnerable to deteriorative erosion caused by overtopping flow.

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Abstract: Rock riprap is commonly used to protect levees, embankment dam, steep channels, and other structures being vulnerable to deteriorative erosion caused by overtopping flow. A review of the literature...

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49 citations

Cites background or methods from "Prediction of local scour depth dow..."

..., 2013), sediment motion (Bhattacharya and Solomatine, 2005; Kumar, 2012), scour depth estimation (Etemad-Shahidi and Ghaemi, 2011; Keshavarzi et al., 2012; Najafzadeh and Lim, 2015; Najafzadeh et al., 2017), and flow prediction in straight compound channels (Zahiri and Azamathulla, 2014)....
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...Later, the GEP approach entered in hydraulic engineering as a powerful platform for the evaluation of scouring problems (Najafzadeh et al., 2016, 2017)....
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...…at al., 2013), sediment motion (Bhattacharya and Solomatine, 2005; Kumar, 2012), scour depth estimation (Etemad-Shahidi and Ghaemi, 2011; Keshavarzi et al., 2012; Najafzadeh and Lim, 2015; Najafzadeh et al., 2017), and flow prediction in straight compound channels (Zahiri and Azamathulla, 2014)....
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Journal Article•DOI•

Enhanced artificial neural network with Harris hawks optimization for predicting scour depth downstream of ski-jump spillway

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Saad Sh. Sammen, Mohammad Ali Ghorbani¹, Anurag Malik², Yazid Tikhamarine³, Mohammad AmirRahmani⁴, Nadhir Al-Ansari⁵, Kwok Wing Chau⁶ - Show less +3 more•Institutions (6)

Istanbul Technical University¹, Punjab Agricultural University², University of Science and Technology Houari Boumediene³, University of Tabriz⁴, Luleå University of Technology⁵, Hong Kong Polytechnic University⁶

27 Jul 2020-Applied Sciences

TL;DR: In this paper, a hybrid ANN-HHO model was proposed to enhance the performance of an artificial neural network (ANN) to predict the scour depth downstream of the ski-jump spillway.

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Abstract: A spillway is a structure used to regulate the discharge flowing from hydraulic structures such as a dam. It also helps to dissipate the excess energy of water through the still basins. Therefore, it has a significant effect on the safety of the dam. One of the most serious problems that may be happening below the spillway is bed scouring, which leads to soil erosion and spillway failure. This will happen due to the high flow velocity on the spillway. In this study, an alternative to the conventional methods was employed to predict scour depth (SD) downstream of the ski-jump spillway. A novel optimization algorithm, namely, Harris hawks optimization (HHO), was proposed to enhance the performance of an artificial neural network (ANN) to predict the SD. The performance of the new hybrid ANN-HHO model was compared with two hybrid models, namely, the particle swarm optimization with ANN (ANN-PSO) model and the genetic algorithm with ANN (ANN-GA) model to illustrate the efficiency of ANN-HHO. Additionally, the results of the three hybrid models were compared with the traditional ANN and the empirical Wu model (WM) through performance metrics, viz., mean absolute error (MAE), root mean square error (RMSE), coefficient of correlation (CC), Willmott index (WI), mean absolute percentage error (MAPE), and through graphical interpretation (line, scatter, and box plots, and Taylor diagram). Results of the analysis revealed that the ANN-HHO model (MAE = 0.1760 m, RMSE = 0.2538 m) outperformed ANN-PSO (MAE = 0.2094 m, RMSE = 0.2891 m), ANN-GA (MAE = 0.2178 m, RMSE = 0.2981 m), ANN (MAE = 0.2494 m, RMSE = 0.3152 m) and WM (MAE = 0.1868 m, RMSE = 0.2701 m) models in the testing period. Besides, graphical inspection displays better accuracy of the ANN-HHO model than ANN-PSO, ANN-GA, ANN, and WM models for prediction of SD around the ski-jump spillway.

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46 citations

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References

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Open Access

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Applied regression analysis

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Norman R. Draper

01 Jan 1998

5,511 citations

Monograph•DOI•

Applied Regression Analysis: Draper/Applied Regression Analysis

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Norman R. Draper, Harry Smith

09 Apr 1998

3,103 citations

"Prediction of local scour depth dow..." refers methods in this paper

...Hence, on the contrary to the primary stepwise regression of Draper and Smith (1998), EPR technique is not linear (Mesbahi et al. 2017)....
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Journal Article•

Gene Expression Programming: A New Adaptive Algorithm for Solving Problems.

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Cândida Ferreira

01 Jan 2001-Complex Systems

TL;DR: Gene expression programming, a genotype/phenotype genetic algorithm (linear and ramified), is presented here for the first time as a new technique for the creation of computer programs with high efficiency that greatly surpasses existing adaptive techniques.

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Abstract: Gene expression programming, a genotype/phenotype genetic algorithm (linear and ramified), is presented here for the first time as a new technique for the creation of computer programs. Gene expression programming uses character linear chromosomes composed of genes structurally organized in a head and a tail. The chromosomes function as a genome and are subjected to modification by means of mutation, transposition, root transposition, gene transposition, gene recombination, and oneand two-point recombination. The chromosomes encode expression trees which are the object of selection. The creation of these separate entities (genome and expression tree) with distinct functions allows the algorithm to perform with high efficiency that greatly surpasses existing adaptive techniques. The suite of problems chosen to illustrate the power and versatility of gene expression programming includes symbolic regression, sequence induction with and without constant creation, block stacking, cellular automata rules for the density-classification problem, and two problems of boolean concept learning: the 11-multiplexer and the GP rule problem.

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1,887 citations

"Prediction of local scour depth dow..." refers background in this paper

...…g) (10)ds∕b = f (Re, h∕b, l∕b, d50∕b, Frd , g ) (11)Re = Ub in which, M, C(i,j), Ct, and T(j) are, respectively, the selection range, value returned by the individual chromosome i for the jth fitness case, total number of fitness cases, and the target value for the jth fitness case (Ferreira 2001)....
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...A sort of the ETs may exhibit the corresponding features, and consequently may adapt to the specific problem they are considered to deal with (Ferreira 2001, 2006)....
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Journal Article•DOI•

Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art

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David A. Van Veldhuizen¹, Gary B. Lamont²•Institutions (2)

Air Force Research Laboratory¹, Air Force Institute of Technology²

01 Jun 2000-Evolutionary Computation

TL;DR: The intent is to rigorously define multiobjective optimization problems and certain related concepts, present an MOEA classification scheme, and evaluate the variety of contemporary MOEAs.

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Abstract: Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade, a variety of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define multiobjective optimization problems and certain related concepts, present an MOEA classification scheme, and evaluate the variety of contemporary MOEAs. Current MOEA theoretical developments are evaluated; specific topics addressed include fitness functions, Pareto ranking, niching, fitness sharing, mating restriction, and secondary populations. Since the development and application of MOEAs is a dynamic and rapidly growing activity, we focus on key analytical insights based upon critical MOEA evaluation of current research and applications. Recommended MOEA designs are presented, along with conclusions and recommendations for future work.

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1,241 citations

"Prediction of local scour depth dow..." refers methods in this paper

...The model selection is made through OPTImized Multi-Objective Genetic Algorithm (OPTIMOGA – Laucelli and Giustolisi 2011) that is, in fact, according to the Pareto dominance criterion (Pareto 1896; Van Veldhuizen and Lamont 2000) to perform multiobjective optimization....
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Induction of model trees for predicting continuous classes

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Yong Wang, Ian H. Witten

01 Oct 1996

793 citations

"Prediction of local scour depth dow..." refers background or methods in this paper

...MT divides the input into several smaller domains and a model of linear multivariable regression is taken into account for each selected domain (EtemadShahidi et al. 2011; Mesbahi et al. 2017; Quinlan 1992; Wang and Witten 1997; Yasa and Etemad-Shahidi 2014)....
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...In Equation (14), sd() is the standard deviation (Wang and Witten 1997)....
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...On the basis of the domain-splitting criterion, different approaches like M5 model have been widely developed and employed to take the advantages of the MT approach (Quinlan 1992; Wang and Witten 1997)....
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...M∑ i=1 �� ds∕b � i(Predicted) − � ds∕b � (Predicted) �2 This process is terminated when the SDR is smaller than a selected percentage of the standard deviation of the original data-set (Quinlan 1992; Wang and Witten 1997)....
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...(Predicted) �2 This process is terminated when the SDR is smaller than a selected percentage of the standard deviation of the original data-set (Quinlan 1992; Wang and Witten 1997)....
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